Known typical methods to categorize input data into two groups include techniques described in Patent Literature (PTL) 1 and Non Patent Literature (NPL) 1.
A soft margin classification system described in PTL 1 is configured to determine parameters including a weight vector and a bias in every data vector of a training set and determine a minimum non-negative value of slack variables for each data vector on the basis of a plurality of constraints. The soft margin classification system described in PTL 1 is configured to determine a minimum value of a cost function so as to satisfy a plurality of constraints.
A method described in NPL 1 is to, when input data cannot be linearly separated, map a pattern into a finite or an infinite dimensional feature space, and to perform linear separation on the feature space.